Computer games are prevalent as entertainment. Machine learning
techniques can be used to help the computer player become smarter by
learning from its experiences. For example, the computer player can
learn which moves to make in different situations.

This project aims to investigate machine learning techniques for
computer games.
More specifically, the objectives are:

In the AI course the students should have learned the fundamental
concepts in search, knowledge representation, and a decision-tree
learning algorithm, for example, (Russell & Norvig, 2003, p. 653-660).

The decision-tree learning algorithm in (Russell & Norvig, 2003,
p. 653-660) is based on Quinlan's (1986) ID3 algorithm. Given a
dataset with each data instance labeled with a class, the algorithm
recursively finds an attribute that can "best" split the instances
into homogeneous subsets with respect to the class labels. The
learned tree can then be used to predict class labels of instances
that are not used during the learning process.